1. Technical Field
One or more embodiments described herein relate generally to enhancing analytical performance. More specifically, one or more embodiments relate to identifying contributing factors associated with a metric anomaly.
2. Background and Relevant Art
Network users access millions of websites daily for a variety of purposes. Network users access websites for purposes such as commerce, information, and entertainment. In fact, it is not uncommon for network users to conduct a large portion of their daily tasks (e.g., shopping, news, recipes, exercise) via various websites. Additionally, users access networks to transfer files, submit search queries, upload pictures and other electronic media, send social network posts, or to utilize various “web-enabled” devices. Users utilize various network connections and servers to perform these tasks in addition to countless other tasks.
In light of widespread and daily network usage, administrators and marketers generally perform data analytics in association with actions performed by various network users in connection with one or more websites or client applications. Advancements in the ability to digitally track user interactions with websites provides administrators and marketers with insights into website usage and statistics not available in the pre-digital age. Occasionally, data analytics reveals anomalies associated with a particular type of user action performed in connection with a website, web page, or client application. For example, an anomaly can be an unexpected increase or decrease associated with a particular type of user action performed in connection with a website or application. To illustrate, a webpage may include an embedded video that, for the last month, web page visitors have played an average of 10 times a day. Then, one day, web page visitors may play the embedded video 50 times. This increase in the number of times web page visitors play the embedded video is generally considered an anomaly because it is an outlier compared to the expected number of plays.
While administrators and marketers can generally identify an anomaly with ease, determining why the anomaly occurred is typically a complex and time consuming task even with the advanced data that digital analytics provides. For example, in response to identifying an anomaly associated with a decrease in the number of website visitors who land on a particular web page within the website, a website administrator generally has to run dozens or even hundreds of reports and queries in order to identify the factors that contributed to the decrease. It may take days, if not weeks, for the website administrator to run and review the results of these reports and queries in order to determine that, for example, the decrease in the number of website visitors who land on the particular webpage was due to factors including a loss of website traffic from a particular search engine (e.g., perhaps due to a change in the website's search engine optimization related to that search engine), and a loss of web traffic from a particular geographic region (e.g., perhaps due to a change in a marketing campaign focused on that particular geographic region). The amount of data and the complexity of data that digital analytics provides often can obscure the sources of the anomalies.
The foregoing issues associated with identifying contributing factors to anomalies are often exacerbated when dealing with multi-variable metrics. For example, when identifying contributing factors associated with multi-variable metrics such as page views per visitor, average time spent on a page, etc., the metric with the largest deviation is often not the largest contributing factor related to the anomaly in the multi-variable metric.
Thus, there are several disadvantages to current methods for identifying factors leading to anomalies, particularly when dealing with multi-variable metrics.